

import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
lowercase			:				List[str]   =     logging.get_logger(__name__)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					List[Any]      		=					WavaVecaForSequenceClassification.from_pretrained(lowerCamelCase_     ,				config=lowerCamelCase_		)
							A      :					Dict      		=					downstream_dict['''projector.weight''']
							A      :					Dict      		=					downstream_dict['''projector.bias''']
							A      :					List[Any]      		=					downstream_dict['''model.post_net.linear.weight''']
							A      :					List[Any]      		=					downstream_dict['''model.post_net.linear.bias''']
							return model






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Any      		=					WavaVecaForAudioFrameClassification.from_pretrained(lowerCamelCase_     ,				config=lowerCamelCase_		)
							A      :					List[Any]      		=					downstream_dict['''model.linear.weight''']
							A      :					Optional[int]      		=					downstream_dict['''model.linear.bias''']
							return model






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Dict      		=					WavaVecaForXVector.from_pretrained(lowerCamelCase_     ,				config=lowerCamelCase_		)
							A      :					str      		=					downstream_dict['''connector.weight''']
							A      :					Dict      		=					downstream_dict['''connector.bias''']
							for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
														A      :					Union[str, Any]      		=					downstream_dict[
														    F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
														]
														A      :					Optional[int]      		=					downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

							A      :					List[str]      		=					downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
							A      :					List[Any]      		=					downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
							A      :					int      		=					downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
							A      :					str      		=					downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
							A      :					int      		=					downstream_dict['''objective.W''']
							return model






@torch.no_grad()
def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Tuple      		=					torch.load(lowerCamelCase_     ,				map_location='''cpu'''		)

							A      :					Union[str, Any]      		=					checkpoint['''Downstream''']

							A      :					str      		=					WavaVecaConfig.from_pretrained(lowerCamelCase_		)
							A      :					Tuple      		=					WavaVecaFeatureExtractor.from_pretrained(
							    lowerCamelCase_     ,				return_attention_mask=lowerCamelCase_     ,				do_normalize=lowerCamelCase_		)

							A      :					List[Any]      		=					hf_config.architectures[0]
							if arch.endswith('''ForSequenceClassification'''		):
														A      :					Dict      		=					convert_classification(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)
							elif arch.endswith('''ForAudioFrameClassification'''		):
														A      :					List[str]      		=					convert_diarization(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)
							elif arch.endswith('''ForXVector'''		):
														A      :					Tuple      		=					convert_xvector(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)
							else:
														raise NotImplementedError(F'S3PRL weights conversion is not supported for {arch}'		)

							if hf_config.use_weighted_layer_sum:
														A      :					Union[str, Any]      		=					checkpoint['''Featurizer''']['''weights''']

							hf_feature_extractor.save_pretrained(lowerCamelCase_		)
							hf_model.save_pretrained(lowerCamelCase_		)


if __name__ == "__main__":
		lowercase			:				Optional[int]   =     argparse.ArgumentParser()
		parser.add_argument(
		    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
		)
		parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
		parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
		parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
		lowercase			:				Tuple   =     parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)









from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
		import timm


if is_torch_available():
		from torch import Tensor







class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	'''pixel_values'''
							UpperCAmelCase_						:		List[str]								=	False
							UpperCAmelCase_						:		Tuple								=	TimmBackboneConfig




							def __init__(					self    ,						__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       int:
														requires_backends(self    ,						'''timm'''						)
														super().__init__(__UpperCAmelCase						)
														A      :					List[str]      		=					config

														if config.backbone is None:
																					raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''						)

														if config.backbone not in timm.list_models():
																					raise ValueError(f'backbone {config.backbone} is not supported by timm.'						)

														if hasattr(__UpperCAmelCase    ,						'''out_features'''						) and config.out_features is not None:
																					raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''						)

														A      :					str      		=					getattr(__UpperCAmelCase    ,						'''use_pretrained_backbone'''    ,						__UpperCAmelCase						)
														if pretrained is None:
																					raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''						)

														# We just take the final layer by default. This matches the default for the transformers models.
														A      :					Optional[int]      		=					config.out_indices if getattr(__UpperCAmelCase    ,						'''out_indices'''    ,						__UpperCAmelCase						) is not None else (-1,)

														A      :					str      		=					timm.create_model(
														    config.backbone    ,						pretrained=__UpperCAmelCase    ,						features_only=config.features_only    ,						in_chans=config.num_channels    ,						out_indices=__UpperCAmelCase    ,						**__UpperCAmelCase    ,						)
														# These are used to control the output of the model when called. If output_hidden_states is True, then
														# return_layers is modified to include all layers.
														A      :					str      		=					self._backbone.return_layers
														A      :					Any      		=					{layer['''module''']: str(__UpperCAmelCase						) for i, layer in enumerate(self._backbone.feature_info.info						)}
														super()._init_backbone(__UpperCAmelCase						)




							@classmethod
							def       snake_case			(					cls    ,						__UpperCAmelCase    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Any:
														requires_backends(cls    ,						['''vision''', '''timm''']						)
														from ...models.timm_backbone import TimmBackboneConfig

														A      :					Optional[Any]      		=					kwargs.pop('''config'''    ,						TimmBackboneConfig()						)

														A      :					str      		=					kwargs.pop('''use_timm_backbone'''    ,						__UpperCAmelCase						)
														if not use_timm:
																					raise ValueError('''use_timm_backbone must be True for timm backbones'''						)

														A      :					Optional[int]      		=					kwargs.pop('''num_channels'''    ,						config.num_channels						)
														A      :					List[str]      		=					kwargs.pop('''features_only'''    ,						config.features_only						)
														A      :					Optional[int]      		=					kwargs.pop('''use_pretrained_backbone'''    ,						config.use_pretrained_backbone						)
														A      :					Optional[int]      		=					kwargs.pop('''out_indices'''    ,						config.out_indices						)
														A      :					int      		=					TimmBackboneConfig(
														    backbone=__UpperCAmelCase    ,						num_channels=__UpperCAmelCase    ,						features_only=__UpperCAmelCase    ,						use_pretrained_backbone=__UpperCAmelCase    ,						out_indices=__UpperCAmelCase    ,						)
														return super()._from_config(__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       Any:
														pass






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						**__UpperCAmelCase						)		->       Union[BackboneOutput, Tuple[Tensor, ...]]:
														A      :					Optional[int]      		=					return_dict if return_dict is not None else self.config.use_return_dict
														A      :					str      		=					(
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)
														A      :					Any      		=					output_attentions if output_attentions is not None else self.config.output_attentions

														if output_attentions:
																					raise ValueError('''Cannot output attentions for timm backbones at the moment'''						)

														if output_hidden_states:
																					# We modify the return layers to include all the stages of the backbone
																					A      :					Dict      		=					self._all_layers
																					A      :					int      		=					self._backbone(__UpperCAmelCase    ,						**__UpperCAmelCase						)
																					A      :					Any      		=					self._return_layers
																					A      :					Union[str, Any]      		=					tuple(hidden_states[i] for i in self.out_indices						)
														else:
																					A      :					Optional[int]      		=					self._backbone(__UpperCAmelCase    ,						**__UpperCAmelCase						)
																					A      :					List[Any]      		=					None

														A      :					str      		=					tuple(__UpperCAmelCase						)
														A      :					int      		=					tuple(__UpperCAmelCase						) if hidden_states is not None else None

														if not return_dict:
																					A      :					Optional[Any]      		=					(feature_maps,)
																					if output_hidden_states:
																												A      :					Tuple      		=					output + (hidden_states,)
																					return output

														return BackboneOutput(feature_maps=__UpperCAmelCase    ,						hidden_states=__UpperCAmelCase    ,						attentions=__UpperCAmelCase						)






